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Impact on Higher Education and College Students in Dijlah University after COVID through E-learning

computer-Aided Design and Applications 2023年第S12期20卷 104-115页

作者： Khudhair, Abdulmunem A. Khudhair, Muthanna A. Jaber, Mustafa Musa Awreed, Youssef Jalil Ali, Mohammed Hasan Al-Hameed, Mazin R. Jassim, Mustafa Mohammed Malik, R.Q. Alkhayyat, Ahmed Hameed, Asaad Shakir Department of Computer Science Dijlah University College Baghdad10021 Iraq Department of information technology Almashreq University Iraq Computer Techniques Engineering Department Faculty of Information Technology Imam Ja’afar Al-Sadiq University Najaf10023 Iraq Computer Technical Engineering National University of science and technology Thi Qar Iraq Department of Medical Instruments Engineering Techniques Al-Farahidi University Baghdad10011 Iraq Medical Instrumentation Techniques Engineering Department Al-Mustaqbal University College Babylon Iraq College of Technical Engineering The Islamic University Najaf Iraq Performance Quality Department Mazaya University College Thi-Qar Iraq

The higher education industry in Iraq had various challenges during the covid-19 outbreak, and this study looked at how remote learning and other relevant technology helped. During the new standard period, a platform with other internet providers helped to keep this sector afloat. The Corona crisis (COVID-19) had a huge impact on Iraqi education, which was revolutionized through the use of e-learning to lessen the danger of pupils contracting COVID-19. learning and its related technology platform, and other online outcomes enabled Iraq's critical higher education businessto survive the covid-19 crisis while also providing insight into the sector's difficulties in the new normal age. © 2023 CAD Solutions, LLC.

关键词： E-learning

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Path Selection Optimization Algorithms for Mobile Agent Based on Push-All-Data Strategy

International Journal of Interactive Mobile Technologies

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International Journal of Interactive Mobile Technologies 2025年第10期19卷 112-128页

作者： Alzyoud, Faisal Tarawneh, Monther Al-Shrouf, Faiz Almaghthawi, Ahmed Alrehaili, Meaad Kanaker, Hasan Department of Computer Science Isra University Amman Jordan Department of Information Technology College of Information Technology and Communications Tafila Technical University Tafila Jordan Department of Software Engineering Faculty of Information Technology Isra University Amman Jordan Department of Computer Science College of Science & Art at Mahayil King Khalid University Abha62529 Saudi Arabia Department of Computer Sciences and Artificial Intelligence Collège of Computer Science and Engineering University of Jeddah Jeddah Saudi Arabia Department of Information Security Faculty of Information Technology Petra University Amman Jordan

With the advent of 5G and 6G technologies and the growing ubiquity of the Internet, the Mobile Agent (MA) paradigm is increasingly seen as a promising alternative to the conventional client-server model. MAs, which are software entities capable of moving and processing data across different systems, offer potential efficiencies in data management. However, their operation in dynamic and mobile environments can lead to challenges, such as incomplete or delayed tasks. This study addresses these issues by focusing on reducing the relocation time of MAs. A numerical procedure and a streamlining strategy were developed to expedite the transfer of an agent from the source to the target hub. Utilizing the itinerary design pattern and the Ant Colony Optimization (ACO) algorithm, implemented via the Java Agent Development Framework (JADE), this study sought the most efficient path for the MA. The proposed algorithm demonstrated a significant improvement, selecting the optimal path in just 271.511 seconds. This performance represents a substantial enhancement over previous approaches using the master-slave design pattern with either the Genetic Algorithm (GA) or the Node Compression Algorithm (NCA). The implications of this improvement are far-reaching, potentially enhancing the efficiency and reliability of data management systems in a variety of applications. © 2025 by the authors of this article. Published under CC-BY.

关键词： information management

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An Enhanced Keylogger Detection Systems Using Recurrent Neural Networks Enabled with Feature Selection Model 7th

An Enhanced Keylogger Detection Systems Using Recurrent Neur...

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7th International Conference on Communication, Device and Networking, ICCDN 2024

作者： Awotunde, Joseph Bamidele Sur, Samarendra Nath Imoize, Agbotiname Lucky Rodríguez, Demóstenes Zegarra Akanji, Boluwatife Department of Computer Science Faculty of Information and Communication Sciences University of Ilorin Kwara State Ilorin240003 Nigeria Department of Electronics and Communication Engineering Sikkim Manipal Institute of Technology Sikkim Manipal University Sikkim India Department of Electrical and Electronics Engineering Faculty of Engineering University of Lagos Akoka Lagos 100213 Nigeria Department of Electrical Engineering and Information Technology Institute of Digital Communication Ruhr University Bochum44801 Germany Department of Computer Science Federal University of Lavras Minas Gerais Lavras Brazil

ISBN: (纸本)9789819764648

Keyloggers are malicious software programs that record keystrokes of users without their consent or knowledge. They can steal sensitive information like credit card numbers and passwords. They pose a significant threat to users’ privacy and security since they capture keystroke data. Keylogger detection systems play a vital role in safeguarding users from cyberattacks and mitigating the potential harm caused by keyloggers. Keylogger detection systems employing deep learning have been widely used for identifying and mitigating cyber threats associated with keyloggers. However, they struggle to identify new or unfamiliar keylogger samples. This study aims to explore deep learning techniques with feature selection model to detect keylogger. This study employs Recurrent Neural Networks (RNNs) using a collection of known keylogger dataset. Furthermore, a correlation-based feature extraction method was applied to identify the most relevant features for the model, highlighting the importance of specific features, such as URG flag Count, ACK flag count, and Idle Mean, in differentiating between benign and keylog classes. The proposed model demonstrates superior accuracy of 0.8763 and precision of 0.8569 compared to a baseline model using Logistic Regression (LR) 0.7721 and 0.7407, respectively, indicating a better balance between accurate classification and minimizing false negatives and false positives. The findings from this study help to improve keylogger detection methods, making them more powerful and efficient. These results suggest that a keylogger-based detection system employing a deep learning approach can be a valuable tool for addressing the complex and evolving landscape of cyber threats related to keylogging. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Recurrent neural networks

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Proposed Privacy Preservation Technique for Color Medical Images

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Intelligent Automation & Soft Computing 2023年第4期36卷 719-732页

作者： Walid El-Shafai Hayam A.Abd El-Hameed Noha A.El-Hag Ashraf A.M.Khalaf Naglaa F.Soliman Hussah Nasser AlEisa Fathi E.Abd El-Samie Department of Electronics and Electrical Communications Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Security Engineering Laboratory Department of Computer SciencePrince Sultan UniversityRiyadh11586Saudi Arabia Electrical Communications Engineering Department Faculty of EngineeringMinia UniversityMinia61111Egypt Higher Institute of Commercial Science Management Information SystemsEl-Mahala El-KobraEgypt Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadhSaudi Arabia

Nowadays,the security of images or information is very *** paper introduces a proposed hybrid watermarking and encryption technique for increasing medical image ***,the secret medical image is encrypted using Advanced Encryption Standard(AES)***,the secret report of the patient is embedded into the encrypted secret medical image with the Least Signiﬁcant Bit(LSB)watermarking *** that,the encrypted secret medical image with the secret report is concealed in a cover medical image,using Kekre’s Median Codebook Generation(KMCG)***,the stego-image obtained is split into 16 ***,it is sent to the *** adopt this strategy to send the secret medical image and report over a network *** proposed technique is assessed with different encryption quality metrics including Peak Signal-to-Noise Ratio(PSNR),Correlation Coefﬁcient(Cr),Fea-ture Similarity Index Metric(FSIM),and Structural Similarity Index Metric(SSIM).Histogram estimation is used to conﬁrm the matching between the secret medical image before and after *** results demonstrate that the proposed technique achieves good performance with high quality of the received medical image and clear image details in a very short processing time.

关键词： LSB steganography AES algorithm KMCG algorithm

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Enhancing Video Anomaly Detection Using Spatio-Temporal Autoencoders and Convolutional LSTM Networks

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SN computer science 2024年第1期5卷 1-13页

作者： Almahadin, Ghayth Subburaj, Maheswari Hiari, Mohammad Sathasivam Singaram, Saranya Kolla, Bhanu Prakash Dadheech, Pankaj Vibhute, Amol D. Sengan, Sudhakar Department of Networks and Cybersecurity Faculty of Information Technology Al Ahliyya Amman University Country Amman Jordan School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Chennai 600127 India Department of Networks and Cybersecurity Information Technology Al Ahliyya Amman University Amman Jordan Department Computing Technology School of Computing SRM Institute of Science and Technology Kattankulathur Campus Tamil Nadu Chennai 603203 India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Guntur 522302 India Department of Computer Science and Engineering Swami Keshvanand Institute of Technology Management and Gramothan (SKIT) Rajasthan Jaipur 302017 India Symbiosis Institute of Computer Studies and Research (SICSR) Symbiosis International (Deemed University) MH Pune 411016 India Department of Computer Science and Engineering PSN College of Engineering and Technology Tamil Nadu Tirunelveli 627152 India

Identifying suspicious activities or behaviors is essential in the domain of Anomaly Detection (AD). In crowded scenes, the presence of inter-object occlusions often complicates the detection of such behaviors. Therefore, developing a robust method capable of accurately detecting and locating anomalous activities within video sequences becomes crucial, especially in densely populated environments. This research initiative aims to address this challenge by proposing a novel approach focusing on AD behaviors in crowded settings. By leveraging a spatio-temporal method, the proposed approach harnesses the power of both spatial and temporal dimensions. This enables the method to effectively capture and analyze the intricate motion patterns and spatial information embedded within the continuous frames of video data. The objective is to create a comprehensive model that can efficiently detect and precisely locate anomalies within complex video sequences, specifically those featuring human crowds. The efficacy of the proposed model will be rigorously evaluated using a benchmark dataset encompassing diverse scenarios involving crowded environments. The dataset is designed to simulate real-world conditions where millions of video footage need to be continuously monitored in real time. The focus is on identifying anomalies, which might occur within short time frames, sometimes as brief as five minutes or even less. Given the challenges posed by the massive volume of data and the requirement for rapid AD, the research emphasizes the limitations of traditional Supervised Learning (SL) methods in this context. © 2024, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Anomaly detection Autoencoders LSTM Machine learning Spatio-temporal features Suspicious activities

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Intelligent Systems for Early Malaria Disease Detection in Patient Cells Using Transfer Learning Approaches 4

Intelligent Systems for Early Malaria Disease Detection in P...

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4th International Conference on Computing, Mathematics and engineering Technologies, iCoMET 2023

作者： Zaman Khan, Gul Ali Shah, Ibrar Farhatullah Hassan, Muhammad Abul Junaid, Hazrat Sardar, Fouzia Uet Department of Computer Software Engineering Mardan Pakistan School of Automation China University of Geosciences Wuhan430074 China University of Trento Department of Information Engineering and Computer Science Italy University of Malakand Department of Computer Science and Information Technology Pakistan University F Malakand Department of Zoology Dir Lower Pakistan

ISBN: (纸本)9798350335316

Malaria is an infectious disease spread by mosquitoes that effect humans and other animals. It is a massive threat to humanity, with instances growing each year. It is essential to prevent and diagnose malaria immediately and efficiently. For the time being, conventional methods are used for diagnosing malaria in which the patient's blood sample is examined by microscope or by using malaria RTD kits. This approach has several limitations because it requires medical expertise, is expensive, takes a long time, and the results are unsatisfactory. Artificial intelligence-based systems can prevent and help in diagnose of this infectious disease. Because of these limitations, the proposed work has proposed an AI-based diagnosis system that can detect malaria parasites immediately and efficiently. In the proposed experiment, we have applied four different pre-trained deep learning models on the image dataset with some preprocessing and optimization techniques for malaria parasite detection. After investigations, evaluation matrices such as precision, Recall, F1-score, sensitivity, and specificity are used to measure the performance of the proposed models. The Inception-Resnet outperformed by achieving 95% accuracy, VGG16 achieved 92% accuracy, inception achieved 93% accuracy, and VGG19 achieved 91% accuracy. The positive outcomes of this study show that this approach performs much better than the approaches currently used. Furthermore, the proposed method is relevant to health experts for screening purposes. © 2023 IEEE.

关键词： Diseases

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Identification and Visualization of Spatial and Temporal Trends in Textile Industry

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computers, Materials & Continua 2023年第2期74卷 4165-4181页

作者： Umair Yousaf Muhammad Asif Shahbaz Ahmed Noman Tahir Azeem Irshad Akber Abid Gardezi Muhammad Shafiq Jin-Ghoo Choi Habib Hamam Department of Computer Science National Textile UniversityFaisalabad38000Pakistan Department of Computer Science and Software Engineering International Islamic UniversityIslamabad44000Pakistan Department of Computer Science COMSATS University IslamabadIslamabad45550Pakistan Department of Information and Communication Engineering Yeungnam UniversityGyeongsan38541Korea Faculty of Engineering Uni de MonctonMonctonNB E1A3E9Canada International Institute of Technology and Management Commune d’AkandaBPLibreville1989Gabon School of Electrical Engineering Department of Electrical and Electronic Eng.ScienceUniversity of JohannesburgJohannesburg2006South Africa Spectrum of Knowledge Production&Skills Development Sfax3027Tunisia

The research volume increases at the study rate,causing massive text *** to these enormous text corpora,we are drowning in data and starving for ***,recent research employed different text mining approaches to extract information from this text *** proposed approaches extract meaningful and precise phrases that effectively describe the text’s *** extracted phrases are commonly termed ***,these key phrases are employed to determine the different fields of study ***,these key phrases can also be used to determine the spatiotemporal trends in the various research *** this research,the progress of a research field can be better revealed through spatiotemporal bibliographic trend ***,an effective spatiotemporal trend extraction mechanism is required to disclose textile research trends of particular regions during a specific *** study collected a diversified dataset of textile research from 2011–2019 and different countries to determine the research *** data was collected from various open access ***,this research determined the spatiotemporal trends using quality *** research also focused on finding the research collaboration of different countries in a particular research *** research collaborations of other countries’researchers show the impact on import and export of those *** visualization approach is also incorporated to understand the results better.

关键词： Text mining spatiotemporal trend research collaboration

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Fast Mesh Reconstruction from Single View Based on GCN and Topology Modification

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computer Systems science & engineering 2023年第5期45卷 1695-1709页

作者： Xiaorui Zhang Feng Xu Wei Sun Yan Jiang Yi Cao Wuxi Research Institute Nanjing University of Information Science&TechnologyWuxi214100China Engineering Research Center of Digital Forensics Ministry of EducationJiangsu Engineering Center of Network MonitoringSchool of Computer and SoftwareNanjing University of Information Science&TechnologyNanjing210044China Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology(CICAEET) Nanjing University of Information Science&TechnologyNanjing210044China School of Automation Nanjing University of Information Science&TechnologyNanjing210044China Department of Electrical and Computer Engineering University of WindsorWindsorONN9B 3P4Canada

3D reconstruction based on single view aims to reconstruct the entire 3D shape of an object from one *** existing methods reconstruct the mesh surface of complex objects,the surface details are difficult to predict and the reconstruction visual effect is poor because the mesh representation is not easily integrated into the deep learning framework;the 3D topology is easily limited by predefined templates and inflexible,and unnecessary mesh self-intersections and connections will be generated when reconstructing complex topology,thus destroying the surface details;the training of the reconstruction network is limited by the large amount of information attached to the mesh vertices,and the training time of the reconstructed network is too *** this paper,we propose a method for fast mesh reconstruction from single view based on Graph Convolutional Network(GCN)and topology *** use GCN to ensure the generation of high-quality mesh surfaces and use topology modification to improve the flexibility of the ***,a feature fusion method is proposed to make full use of the features of each stage of the image *** use 3D open dataset ShapeNet to train our network and add a new weight parameter to speed up the training *** experiments demonstrate that our method can not only reconstruct object meshes on complex topological surfaces,but also has better qualitative and quantitative results.

关键词： 3D surface reconstruction deep learning GCN topology modification end-to-end framework

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Fiber-optic sensor based on Bragg grating and 3D printing technique for monitoring the heart rate of the human body

Fiber-optic sensor based on Bragg grating and 3D printing te...

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Optical Sensors 2023

作者： Krizan, Daniel Kostelansky, Michal Mohammed, Mazin Abed Martinek, Radek Nedoma, Jan Department of Telecommunications Faculty of Electrical Engineering and Computer Science VSB-Technical University of Ostrava Czech Republic Department of Cybernetics and Biomedical Engineering Faculty of Electrical Engineering and Computer Science VSB-Technical University of Ostrava Czech Republic College of Computer Science and Information Technology University of Anbar Anbar Iraq

ISBN: (数字)9781510662650

ISBN: (纸本)9781510662643

This paper discusses a proposed fiber-optic sensor that was based on the combination of the material called polyethylene terephthalate glycol (PETG) and a fiber Bragg grating (FBG). The practical experiments were conducted under laboratory conditions with a group of 6 volunteers. The data were compared with conventional electrocardiogram (ECG) and processed using a Bland-Altman method. The results described in this paper show that the 3D printing technique can be used for encapsulating the FBG and for the purpose of biomedical engineering. © 2023 SPIE.

关键词： Fiber Bragg gratings

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Exploring household food security through institutional factors: A statistical and mathematical analysis

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Journal of Intelligent and Fuzzy Systems 2024年第4期46卷 9179-9195页

作者： Khan, Younas Ashraf, Shahzaib Farman, Muhammad Abdallah, Suhad Ali Osman Department of Sociology Kohat University of Science and Technology Kohat Pakistan Institute of Mathematics Khawaja Fareed University of Engineering and Information Technology Rahim Yar Khan Pakistan Department of Mathematics Faculty of Arts and Science Near East University Turkey Department of Computer Science and Mathematics Lebanese American University Beirut Lebanon Applied College Khamis Mushait King Khalid University Abha Saudi Arabia

Achieving household food security is the tumbling issue of the century. This article explores the factors affecting household food security and solutions by utilizing a synergy of statistical and mathematical models. The methodology section is divided into two portions namely sociological and mathematical methods. Sociologically, 379 household heads were interviewed through structured questions and further analyzed in terms of descriptive and binary logistic regression. The study found that 4 independent variables (poverty, poor governance, militancy, and social stratification) showed a significant association (P = 0.000) to explain variations in the dependent variable (household FS). The Omnibus test value (χ2= 102.386;P = 0.000) demonstrated that the test for the entire model against constant was statistically significant. Therefore, the set of predictor variables could better distinguish the variation in household FS. The Nagelkerke's R Square (R2 = .333) helps to interpret that the prediction variable and the group variables had a strong relationship. Moreover, 23% to 33% variation in FS was explained by the grouping variables (Cox and Snell R2 = 0.237 and Nagelkerke's R2 = 0.333). The significant value of Wald test results for each variable confirmed that the grouping variables (poor governance P = 0.004, militancy P = 0.000, social stratification P = 0.021 and poverty P = 0.000) significantly predicted FS at the household level. Mathematically, all the statistics were validated further through the application of spherical fuzzy mathematics (TOPIS and MADM) to explore what factors are affecting household FS. Thus, the study found that F3 (poverty) > F2 (militancy) > F4 (social stratification) > F1 (poor governance) respectively. Thus, it could be concluded from these findings that the prevalence of poverty dysfunctional all the channels of household FS at the macro and micro levels. Therefore, a sound and workable model to eradicate poverty in the study area by

关键词： Food supply

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